Python大 pandas ：逐行填充数据框 [英] Python pandas: fill a dataframe row by row

问题描述

将一行添加到 pandas.DataFrame 对象的简单任务似乎很难完成。有3个stackoverflow问题与此相关，其中没有一个给出工作答案。

The simple task of adding a row to a pandas.DataFrame object seems to be hard to accomplish. There are 3 stackoverflow questions relating to this, none of which give a working answer.

这是我正在努力做的。我有一个DataFrame，我已经知道它的形状以及行和列的名称。

Here is what I'm trying to do. I have a DataFrame of which I already know the shape as well as the names of the rows and columns.

>>> df = pandas.DataFrame(columns=['a','b','c','d'], index=['x','y','z'])
>>> df
     a    b    c    d
x  NaN  NaN  NaN  NaN
y  NaN  NaN  NaN  NaN
z  NaN  NaN  NaN  NaN

现在，我有一个函数迭代地计算行的值。如何使用字典或 pandas.Series 填写其中一行？以下是失败的各种尝试：

Now, I have a function to compute the values of the rows iteratively. How can I fill in one of the rows with either a dictionary or a pandas.Series ? Here are various attempts that have failed:

>>> y = {'a':1, 'b':5, 'c':2, 'd':3} 
>>> df['y'] = y
AssertionError: Length of values does not match length of index

显然，它尝试添加一列而不是一行。

Apparently it tried to add a column instead of a row.

>>> y = {'a':1, 'b':5, 'c':2, 'd':3} 
>>> df.join(y)
AttributeError: 'builtin_function_or_method' object has no attribute 'is_unique'

非常不知情的错误信息。

Very uninformative error message.

>>> y = {'a':1, 'b':5, 'c':2, 'd':3} 
>>> df.set_value(index='y', value=y)
TypeError: set_value() takes exactly 4 arguments (3 given)

显然这只是为了在数据框中设置个别值。

Apparently that is only for setting individual values in the dataframe.

>>> y = {'a':1, 'b':5, 'c':2, 'd':3} 
>>> df.append(y)
Exception: Can only append a Series if ignore_index=True

嗯，我不想忽略索引，否则这里是结果：

Well, I don't want to ignore the index, otherwise here is the result:

>>> df.append(y, ignore_index=True)
     a    b    c    d
0  NaN  NaN  NaN  NaN
1  NaN  NaN  NaN  NaN
2  NaN  NaN  NaN  NaN
3    1    5    2    3

它将列名与值对齐，但丢失了行标签。

It did align the column names with the values, but lost the row labels.

>>> y = {'a':1, 'b':5, 'c':2, 'd':3} 
>>> df.ix['y'] = y
>>> df
                                  a                                 b  \
x                               NaN                               NaN
y  {'a': 1, 'c': 2, 'b': 5, 'd': 3}  {'a': 1, 'c': 2, 'b': 5, 'd': 3}
z                               NaN                               NaN

                                  c                                 d
x                               NaN                               NaN
y  {'a': 1, 'c': 2, 'b': 5, 'd': 3}  {'a': 1, 'c': 2, 'b': 5, 'd': 3}
z                               NaN                               NaN

这也失败了。

那么你怎么样这样做？

推荐答案

df ['y'] 将设置列

因为您要设置一行，请使用 .loc

since you want to set a row, use .loc

请注意， .ix 是等效的在这里，您的失败是因为您尝试为行 y 中的每个元素分配字典
可能不是您想要的;转换成一个系列告诉熊猫
你想对齐输入（例如你不需要指定所有的元素）

Note that .ix is equivalent here, yours failed because you tried to assign a dictionary to each element of the row y probably not what you want; converting to a Series tells pandas that you want to align the input (for example you then don't have to to specify all of the elements)

In [7]: df = pandas.DataFrame(columns=['a','b','c','d'], index=['x','y','z'])

In [8]: df.loc['y'] = pandas.Series({'a':1, 'b':5, 'c':2, 'd':3})

In [9]: df
Out[9]: 
     a    b    c    d
x  NaN  NaN  NaN  NaN
y    1    5    2    3
z  NaN  NaN  NaN  NaN

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